Linking common human diseases to their phenotypes; development of a resource for human phenomics.

BACKGROUND: In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. METHODS: We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10-phenotype associations from existing resources containing known relationships. RESULTS: We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease-phenotype associations, measuring their similarity to disease-phenotype associations found in public databases, and assessing how well they could be used to recover gene-disease associations using phenotype similarity. CONCLUSION: We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at https://doi.org/10.5281/zenodo.4726713 .

Combining lexical and context features for automatic ontology extension.

BACKGROUND: Ontologies are widely used across biology and biomedicine for the annotation of databases. Ontology development is often a manual, time-consuming, and expensive process. Automatic or semi-automatic identification of classes that can be added to an ontology can make ontology development more efficient. RESULTS: We developed a method that uses machine learning and word embeddings to identify words and phrases that are used to refer to an ontology class in biomedical Europe PMC full-text articles. Once labels and synonyms of a class are known, we use machine learning to identify the super-classes of a class. For this purpose, we identify lexical term variants, use word embeddings to capture context information, and rely on automated reasoning over ontologies to generate features, and we use an artificial neural network as classifier. We demonstrate the utility of our approach in identifying terms that refer to diseases in the Human Disease Ontology and to distinguish between different types of diseases. CONCLUSIONS: Our method is capable of discovering labels that refer to a class in an ontology but are not present in an ontology, and it can identify whether a class should be a subclass of some high-level ontology classes. Our approach can therefore be used for the semi-automatic extension and quality control of ontologies. The algorithm, corpora and evaluation datasets are available at https://github.com/bio-ontology-research-group/ontology-extension.

Ontology-based prediction of cancer driver genes.

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.